Combining MEA-Net and LAP-Net for Pneumoconiosis Staging Framework.

OBJECTIVE: Pneumoconiosis is a common and highly hazardous occupational disease. The staging of pneumoconiosis is mainly carried out by experienced doctors on the basis of the shadows and textures on lung x-ray films. Despite well-defined criteria, the process remains influenced by individual clinical judgment. METHODS: To improve the subjective process of pneumoconiosis diagnosis, this study proposes a new deep learning framework for pneumoconiosis staging framework using MEA-Net and LAP-Net. RESULTS: The experimental results show that the accuracy, precision, recall, specificity, F1-score, and area under the curve in the four-stage classification reached 95.24%, 95.15%, 95.15%, 90.58%, 94.85%, and 98.87%, respectively. CONCLUSION: The proposed method can help doctors to identify the different stages of pneumoconiosis more accurately in the diagnosis of the disease.